Modeling of forecasting and production planning data

ABSTRACT

A forecast class is defined that represents forecasts of different types and identifies relationships of a forecast with various entities related to the forecast.

FIELD OF THE INVENTION

This invention relates generally to data modeling, and more particularlyto modeling of forecasting and production planning data.

COPYRIGHT NOTICE/PERMISSION

A portion of the disclosure of this patent document contains materialwhich is subject to copyright protection. The copyright owner has noobjection to the facsimile reproduction by anyone of the patent documentor the patent disclosure as it appears in the Patent and TrademarkOffice patent file or records, but otherwise reserves all copyrightrights whatsoever. The following notice applies to the software and dataas described below and in the drawings hereto: Copyright© 2001, SiebelSystems, Inc., All Rights Reserved.

BACKGROUND OF THE INVENTION

Forecasts are the foundation of every company's business plans. Accurateforecasts enable a company to maintain appropriate staffing levels, setrealistic sales targets, create effective marketing promotion mixes,build budgets that match operating expenses, and keep inventory at theright level to meet, but not exceed, customer demand.

Accurate forecasting must involve a range of activities across differentdepartments within an organization and/or different organizations. Forexample, in a manufacturing company, the forecasting process may requirecollaboration among its manufacturing, sales and marketing departments,as well as outside companies such as raw material suppliers,wholesalers, etc. Typically, the approaches to forecasting may varysignificantly from company-to-company and division-to-division. Whileforecasting may only involve a simple query in some companies ordivisions, it can be an extensive planning process in other companies ordivisions. Forecasting may be very high level in some organizations, andvery detailed in other organizations. As a result, forecast data modelsmaintained by different companies and divisions typically supportvarying levels of detail of the forecast data being stored.

Further, individual companies usually store forecast data in their ownunique way. For example, a raw material supplier may organize forecastdata in a way that is very different from the way that a manufacturingcompany may organize forecast data. Even within a single manufacturingcompany, that company may use many different application programs thatemploy very different schemas and data models. For example, a demandforecasting program may use a data model that is very different from thedata model used by a supply forecasting program. The use of customizeddata models by a company and by internal applications has the advantagethat it allows information to be modeled in a way that is appropriatefor the business needs of the company. Unfortunately, because of thisdiversity in the data models, it is not easy for the company to shareits information with other companies or for internal applications toshare their information.

Various attempts have been made to define standard data models so thatinformation can be more easily shared between different organizationsand applications to allow the consolidation of their disparate forecastprocesses into a single, comprehensive forecast. However, these datamodels have not been able to achieve sufficient data integration andsimplicity. As a result, existing forecasts are typically inaccurate,ineffectual and difficult to verify.

SUMMARY OF THE INVENTION

The present invention relates to various aspects for modeling forecastdata.

According to one aspect of the present invention, a forecast class isdefined that represents forecasts of different types and identifiesrelationships of a forecast with various entities related to theforecast.

BRIEF DESCRIPTION OF THE DRAWINGS

The present invention will be understood more fully from the detaileddescription given below and from the accompanying drawings of variousembodiments of the invention, which, however, should not be taken tolimit the invention to the specific embodiments, but are for explanationand understanding only.

FIG. 1 is a block diagram illustrating the interconnection betweenvarious business systems and a universal business application network,according to one embodiment of the present invention.

FIG. 2 is a block diagram illustrating the overall architecture of auniversal business application network, according to one embodiment ofthe present invention.

FIG. 3 is a flow diagram of one embodiment of a process for facilitatingthe sharing of forecast data between two applications.

FIG. 4 is a flow diagram of one embodiment of a process for addingcustom data to a forecast class.

FIGS. 5-16 illustrate one embodiment of a common data model representinga forecast.

FIG. 17 is a block diagram of an exemplary computer system that may beused to perform one or more of the operations described herein.

DETAILED DESCRIPTION OF THE INVENTION

In the following description, numerous details are set forth. It will beapparent, however, to one skilled in the art, that the present inventionmay be practiced without these specific details. In some instances,well-known structures and devices are shown in block diagram form,rather than in detail, in order to avoid obscuring the presentinvention.

Some portions of the detailed descriptions which follow are presented interms of algorithms and symbolic representations of operations on databits within a computer memory. These algorithmic descriptions andrepresentations are the means used by those skilled in the dataprocessing arts to most effectively convey the substance of their workto others skilled in the art. An algorithm is here, and generally,conceived to be a self-consistent sequence of steps leading to a desiredresult. The steps are those requiring physical manipulations of physicalquantities. Usually, though not necessarily, these quantities take theform of electrical or magnetic signals capable of being stored,transferred, combined, compared, and otherwise manipulated. It hasproven convenient at times, principally for reasons of common usage, torefer to these signals as bits, values, elements, symbols, characters,terms, numbers, or the like.

It should be borne in mind, however, that all of these and similar termsare to be associated with the appropriate physical quantities and aremerely convenient labels applied to these quantities. Unlessspecifically stated otherwise as apparent from the following discussion,it is appreciated that throughout the description, discussions utilizingterms such as “processing” or “computing” or “calculating” or“determining” or “displaying” or the like, refer to the action andprocesses of a computer system, or similar electronic computing device,that manipulates and transforms data represented as physical(electronic) quantities within the computer system's registers andmemories into other data similarly represented as physical quantitieswithin the computer system memories or registers or other suchinformation storage, transmission or display devices.

The present invention also relates to apparatus for performing theoperations herein. This apparatus may be specially constructed for therequired purposes, or it may comprise a general purpose computerselectively activated or reconfigured by a computer program stored inthe computer. Such a computer program may be stored in a computerreadable storage medium, such as, but is not limited to, any type ofdisk including floppy disks, optical disks, CD-ROMs, andmagnetic-optical disks, read-only memories (ROMs), random accessmemories (RAMs), EPROMs, EEPROMs, magnetic or optical cards, or any typeof media suitable for storing electronic instructions, and each coupledto a computer system bus.

The algorithms and displays presented herein are not inherently relatedto any particular computer or other apparatus. Various general purposesystems may be used with programs in accordance with the teachingsherein, or it may prove convenient to construct more specializedapparatus to perform the required method steps. The required structurefor a variety of these systems will appear from the description below.In addition, the present invention is not described with reference toany particular programming language. It will be appreciated that avariety of programming languages may be used to implement the teachingsof the invention as described herein.

A machine-readable medium includes any mechanism for storing ortransmitting information in a form readable by a machine (e.g., acomputer). For example, a machine-readable medium includes read onlymemory (“ROM”); random access memory (“RAM”); magnetic disk storagemedia; optical storage media; flash memory devices; electrical, optical,acoustical or other form of propagated signals (e.g., carrier waves,infrared signals, digital signals, etc.); etc.

A data model that provides a common data structure to represent aforecast and allows for customization of the data model in a manner thatfacilitates upgrading of the data model is described. The term“forecast” refers to various forecasting and planning processes withinan organization or across several collaborating organizations. Theinformation that is used in these processes or is generated during theseprocesses is referred to herein as forecast data. The forecasting andplanning processes may include, for example, demand forecasting, revenueforecasting, unit forecasting, sales volume planning, productionplanning, budget planning, etc.

In one embodiment, the data model defines a forecast class thatrepresents forecasts of different types. The different types mayinclude, for example, a demand forecast type and a supply forecast type.A demand forecast may be created by a sales division to predict thedemand for a particular product or service and the resulting revenue. Asupply forecast may be created by a production division to predictinventory requirements. The sales and production divisions may be partsof the same organization or different organizations. In one embodiment,the forecast class includes a designated data element to store data thatindicates whether the elements of the forecast class store data of aspecific forecast type (e.g., a demand forecast type or a supplyforecast type).

In one embodiment, the forecast data model defines relationships of aforecast with various entities related to the forecast. These entitiesmay include, for example, a related business unit (an organization forwhich the forecast is created), a forecast planner (an entity creatingthe forecast), a related product line (a product line or service forwhich the forecast is created), a related product (a product or servicefor which the forecast is created), a related account (an organizationproviding the product or service for which the forecast is created), anda related sales representative (a sales representative of the product orservice for which the forecast is created).

In one embodiment, the forecast data model also defines relationshipswith demand sources for forecasts of the demand type. A demand sourcemay be a business opportunity (e.g., a deal to sell a specific product)or a service request (e.g., a request to provide a specific service).

In one embodiment, the forecast data model associates the forecast withfact data. The fact data may include, for example, the number of units,the unit of measure, the unit price of the product or service for whichthe forecast is created, the total cost of the product or service forwhich the forecast is created, the total revenue that is expected to begenerated by the product or service, the best case estimate of therevenue, and the worst case estimate of the revenue.

The data model models the relationships as attributes associated withforecast. In one embodiment, the forecast data model is specified usinga schema language such as XML Schema.

Based on the relationships defined by the forecast data model, a revenueforecast may be reported by account, product line, product, opportunity,service request, forecast planner, or organization.

In one embodiment, the data model defines a hierarchy of the dataelements for describing a forecast. The data model may define dataelements that are complex. A complex data element is a data element thatcomprises data sub-elements. For example, an address data element may bea complex data element that includes street, city, and state datasub-elements. The data model may specify custom data elements at variousplaces within the hierarchy of data elements. A custom data element isof a custom data element type. The custom data element type initiallydefines no data elements. The data model can be customized by definingcustom data elements that are specific to different applications orsystems. Because the custom data elements are defined at various placeswithin the hierarchy, the customizations of the data model can beassociated with related data elements within the hierarchy.

Thus, the forecast data model provides a common data structure forinterfacing forecast data of various divisions within an organizationand/or forecast data of an organization and collaborating third parties,while allowing for simplified customization of this data structure byindividual companies in accordance with their needs. Hence, the forecastdata model allows companies to maintain, support and upgrade only asingle data model and facilitates efficient data transformations andmappings.

FIG. 1 is a block diagram illustrating the interconnection betweenvarious business systems 100 (business systems utilizing forecastrelated data) and a universal business application network 102,according to one embodiment of the present invention. The universalbusiness application network 100 serves as an integration hub for thebusiness systems 100. The architecture of the universal businessapplication network 102 allows new applications (e.g., new forecastapplications) that access legacy business systems to be developed withminimum customization. The legacy business systems can be provided by asingle business organization or by different business organizations. Theuniversal business application network 102 allows the forecastapplications to exchange information using a forecast data model 104.

In one embodiment, the forecast data model 104 defines a hierarchicaldata structure representing a forecast. This hierarchical data structureincludes data elements that are common to all business systems 100. Inaddition, the hierarchical data structure includes data custom dataelements at various levels of the hierarchy to define data fields thatare specific to each business system 100, thus providing for easycustomization of the forecast data model 104.

In one embodiment, the universal business application network 102 usesthe XML and Web services standards.

FIG. 2 is a block diagram illustrating the overall architecture of auniversal business application network in one embodiment. The hub of theuniversal business application network is an integration server 200 thatconnects to the various business systems 204 (e.g., business systemsutilizing forecast related data) via adapters 202. The integrationserver 200 includes a transport layer 216, a data model 210, atransformation store 214, a business process controller 206, and abusiness process store 208.

The transport layer 216 is a mechanism through which businessinformation is exchanged between the business systems 204 and thebusiness integration server 200. Each business system 204 may have anadapter 202 that is appropriate to the protocol of the transport layer.For example, the transport mechanism may use communications protocolssuch as TCP/IP. The transport layer 216 may provide a messaging servicefor queuing, for guaranteeing delivery of messages, and for handlingboth synchronous and asynchronous messaging. The adapters 202 relayevents from the business systems 204 to the integration server 200 andcan import configurations of the business systems 204 into theintegration server 200. In addition, the universal business applicationnetwork may include encryption and authentication mechanisms to ensurethe security and integrity of the information. For example,authentication will help ensure that a business process is accessing theintended business system, rather than an impostor business system.

The integration server 200 stores the representation of a data model 210(e.g., in an XML schema file) that contains the definition of a forecastclass. The forecast class represents forecasts of different types (e.g.,demand forecasts and supply forecasts) and defines relationships of aforecast with various related entities.

The transformation store 212 contains a model data definition tool(e.g., an XML schema definition tool) to create a definition of the datamodel 210 (e.g., in an XML schema file) and to customize the data model210 when requested by adding custom data fields to the data model 210.The transformation store 212 also contains transformations fortransforming information received from the business systems 204 to theformat used by the data model 210, and vice versa. The transformationsmay be specified as a computer program, an XML Stylesheet LanguageTransform (XSLT), etc.

The business process store 208 contains the business processes that havebeen defined. A business process may be specified as a script, a processflow, an executable program, etc. In one embodiment, the businessprocesses are defined using the Web Services Flow Language (OOWSFL). Thebusiness processes orchestrate a sequence of steps across multipleapplications provided by the business systems 204 to achieve a businessobjective.

The business process controller 206 coordinates the execution of thebusiness processes. The business process controller 206 may instantiatethe forecast class and invoke functions of the resulting object inaccordance with the various business processes. The business processcontroller 206 may also initiate the execution of business processesbased on predefined conditions and events. For example, the businessprocess controller 206 may launch a certain business process each timean alert is received. Although not shown, the business integrationnetwork may provide a standard library of business routines that may beinvoked by the business processes. For example, a standard businessroutine may identify whether a product line and an account are relatedvia a sales representative and create a relationship between the productline and the account if they are related. The integration server 200 mayalso include various tools to facilitate the development of businessprocesses. These tools may aid in the development of transformations,the defining of classes, and the writing of process flows.

FIG. 3 is a flow diagram of one embodiment of a process 300 forfacilitating the sharing of forecast data between two insuranceapplications. The process may be performed by processing logic that maycomprise hardware (e.g., circuitry, dedicated logic, etc.), software(such as run on a general purpose computer system or a dedicatedmachine), or a combination of both. Processing logic may reside on anintegration server such as the integration server 200 of FIG. 2.

Referring to FIG. 3, process 300 begins with processing logic receivinga request from a source system to send forecast data to a target system(processing block 302). For example, forecast data may pertain to ademand forecast created by a sales division, a source system may be afront-end application used by the sales division, and a target systemmay be a back-end application used by a manufacturing division to createa supply forecast based on the forecast data received from the salesdivision. In another example, forecast data may pertain to a supplyforecast created by a manufacturing division, a source system may be aback-end application used by the manufacturing division, and a targetsystem may be a front-end application used by a sales division to adjusttheir demand forecast based on the forecast data received from themanufacturing division.

Next, processing logic transforms the forecast data into a common formatprovided by the forecast class (processing block 304). The forecastclass represents forecasts of different types (e.g., a demand forecasttype and a supply forecast type) and defines relationships of a forecastwith various entities related to the forecast. These entities mayinclude, for example, a related business unit (an organization for whichthe forecast is created), a forecast planner (an entity creating theforecast), a related product line (a product line or service for whichthe forecast is created), a related product (a product or service forwhich the forecast is created), a related account (an organizationproviding the product or service for which the forecast is created), anda related sales representative (a sales representative of the product orservice for which the forecast is created). In one embodiment, theforecast data model also defines relationships with demand sources forforecasts of the demand type. A demand source may be an opportunity or aservice request. In one embodiment, the forecast data model associatesthe forecast with fact data. The fact data may include, for example, theunit price of the product or service for which the forecast is created,the total cost of the product or service for which the forecast iscreated, the total revenue that is expected to be generated by theproduct or service, the best case estimate of the revenue, and the worstcase estimate of the revenue.

Further, processing logic transforms the forecast data from the commonformat into a format recognizable by the target system (processing block306) and sends the resulting forecast data to the target system(processing block 308).

Thus, according to the process 300, the sharing of forecast data betweentwo systems does not require data mapping between the data format of thesource application and the data format of the target application.Instead, the mapping is performed between each system and the commondata model. Furthermore, the process 300 allows various divisions and/ororganizations to share the forecast data in a manner that facilitatesthe consolidation of their disparate forecast processes into a single,comprehensive forecast process.

An exemplary forecast process will now be described to illustrate theoperation of a consolidated forecast process. In particular, a front-endforecast application may create a forecast based on revenue dataprovided by a customer, partner, or sales representative. Based on theforecast data model discussed herein, the revenue may have manyassociated dimensions, depending on the requirements of the company'sforecasting methodology (e.g., the revenue data may reported by product,quantity, unit of measure, account, etc.). In addition, revenueforecasts may be created for sales representatives divisions,organizations, product lines or regions to improve management's abilityto make informed business decisions via reports, charts or analysis.Executives may make revisions to the revenue forecasts and then committhe final forecast number. The revisions may be made using historicaldata that allows the executives to determine trends and create areasonable projection of expected revenue. In addition, data related tothe sales stage of all opportunities in the system may be evaluated todetermine how close forecasted deals are to closing and to furtherassess pipeline coverage and the general health of the opportunitypipeline.

Further, the final forecast data may be exported to the back-end systemof the manufacturing division using the process 300 discussed above. Theback-end system may receive input of the supply planning team andgenerate a supply forecast. Then, the demand planning system of themanufacturing division may analyze the revenue forecast received fromthe front-end system and combine it with the supply forecast generatedby the supply planning systems in order to generate the combinedforecast. The combined forecast may then be transferred to the front-endsystem of the sales division using the process 300 discussed above.

Afterwards, the sales and operations planning meeting may be conducted,in which the executives from the sales and manufacturing divisions jointogether to agree upon a final product forecast, or the “consensusforecast.” This forecast may then be disaggregated by the front-endsystem and presented to the sales division for use in incentivecompensation and quota planning. In addition, the process 300 may beutilized to transfer corresponding portions of the consensus forecast tothe marketing system for use in campaign and promotion planning, to themanufacturing division for use in supply planning, and to the financialdivision for use in budget planning and financial planning.

As discussed above, in one embodiment, each class of the forecast datamodel can be customized for a specific business system or application.

FIG. 4 is a flow diagram of one embodiment of a process for addingcustom data to forecast class. The process may be performed byprocessing logic that may comprise hardware (e.g., circuitry, dedicatedlogic, etc.), software (such as run on a general purpose computer systemor a dedicated machine), or a combination of both. Processing logic mayreside on an integration server such as the integration server 200 ofFIG. 2.

At processing block 402, processing logic retrieves a data definitionschema for the forecast class. The schema may be an XML schema file thatinclude a custom data element of a type that is defined in another file.

At processing block 404, processing logic retrieves the custom dataschema for the types of custom data. The schema may be stored in an XMLschema file that contains the definition for each type of custom data.

Next, processing logic opens the custom data schema (processing block406) and locates the tags relating to the custom data type of interest(processing block 408).

Further, processing logic adds the custom data elements to the locatedtags (processing block 410) and closes the custom data schema with thenewly defined data elements (processing block 412).

One embodiment of a common data model representing a forecast will nowbe described in more detail in conjunction with FIGS. 5-16. One skilledin the art will appreciate that various other common data modelsrepresenting forecast can be used with the present invention withoutloss of generality. In addition, the names of data elements illustratedin FIGS. 5-16 are descriptive of the information stored in the dataelements.

FIG. 5 illustrates the highest level data elements of the forecast classin one embodiment. The highest level data elements include id 502,baseData 504, statusData 506, relatedBusUnit 508, relatedPlanner 510,listOfForecastLineDetail 512, and customData 514.

The id data element 502 may be a unique identifier of a forecast. ThebaseData data element 504 contains general information about theforecast as will be discussed in greater detail below in conjunctionwith FIG. 6.

The statusData data element 506 contains information on the status ofthe forecast (e.g., in progress, archived, etc.). The relatedBusUnitdata element 508 specifies an organization for which the forecast iscreated. The relatedPlanner data element 510 specifies an entitycreating the forecast. This entity may be an employee creating theforecast or a division within which the forecast is created. TherelatedPlanner data element 510 will be discussed in more detail belowin conjunction with FIG. 7.

The listOfForecastLineDetail data element 512 contains detailinformation pertaining to a product line, a product or a service forwhich the forecast is created, as will be discussed in more detail belowin conjunction with FIG. 8.

The customData data element 514 initially contains no data elements, butcustom data elements can be added by defining data elements in theForecastCustomDataType.

FIG. 6 illustrates the data elements of the baseData class in oneembodiment. The data elements of the baseData class include date 602,endDate 604, name 606, periodTypeCode 608, and startDate 610.

The date data element 602 specifies the date for which the forecast iscreated (e.g., the first day of each month or week). The startDate dataelement 610 specifies the beginning of the date range for the forecast(i.e., the first date for which revenue records are pulled into theforecast). The endDate data element 604 defines the last date of thedate range for the forecast. The name data element 606 specifies theforecast name. The periodTypeCode data element 608 defines theperiodicity by which the forecast records are totaled at a summary level(e.g., weekly totals, monthly totals or quarterly totals).

FIG. 7 illustrates the data elements of the ForecastRelatedPlanner classfor the person type, according to one embodiment. The data elements usedin the ForecastRelatedPlanner class include id 702 communicationData704, listOfAddress 708, listOfRelationship 710, customPartyData 716,baseData 718, and customData 722.

The id data element 702 may be a unique identifier of a party. ThecommunicationData data element 704 contains general information on thecommunication data for a party. The listOfAddress data element 708contains the addresses of a party. The listOfRelationship data element710 defines relationships that a party can have with other entities. ThecustomPartyData data element 716 initially contains no data elements,but custom data elements can be added by defining data elements in thePartyCustomData Type. The baseData data element 718 contains generalinformation about the person.

FIG. 8 illustrates the data elements of the ForecastLineDetailType classin one embodiment. The ForecastLineDetailType class provides detailedinformation on a product, product line or service for which the forecastis created. In the following description of the data elements of theForecastLineDetailType class, a product, a product line and a serviceare collectively referred to as a product. The data elements of theForecastLineDetailType class include id 802, baseData 804, factData 806,statusData 808, relatedAccount 810, relatedProduct 812, relatedSalesRep814, listOfRelatedDemandSource 816 and custom data 818. In oneembodiment, the listOfRelatedDemandSource data element 816 is used toindicate whether the other data elements contain data of a demandforecast or supply forecast. In one embodiment, if thelistOfRelatedDemandSource data element 816 contains no data, the otherdata elements are considered to store data of the demand forecast.Otherwise, the data elements will be considered to store data of thesupply forecast.

The id data element 802 may contain a unique identifier of a product forwhich the forecast is created. The baseData data element 804 specifiesgeneral information for the product as will be discussed in more detailbelow in conjunction with FIG. 9. The factData data element 806specifies facts associated with the product or product line as will bediscussed in more detail below in conjunction with FIG. 10. ThestatusData data element 808 indicates the status of the forecast line.The relatedAccount data element 810 contains information about anorganization providing the product as will be discussed in more detailbelow in conjunction with FIG. 11. The relatedProduct data element 812contains information about the product as will be discussed in moredetail below in conjunction with FIG. 12. The relatedSalesRep dataelement 814 contains information about a sales representative associatedwith the product as will be discussed in more detail below inconjunction with FIG. 13. The listOfRelatedDemandSource data element 816identifies sources of the demand for the product as will be discussed inmore detail below in conjunction with FIGS. 14-16. The customData dataelement 818 initially contains no data elements, but custom dataelements can be added by defining data elements in theForecastLineCustomData Type.

FIG. 9 illustrates the data elements of theForecastLineDetailBaseDataType class in one embodiment. These dataelements include the currencyCode 902, the date 904, the quantity 906,and the unitOfMeasureCode 908.

The currencyCode data element 902 specifies a forecast uniform currencycode. The date 904 data element indicates the date for which theforecast associated with this product is created. The quantity dataelement 906 defines the quantity of the product. The unitOfMeasureCodedata element 908 specifies the unit of measure for the product quantity(e.g., a uniform unit of measure or a detail unit of measure).

FIG. 10 illustrates the data elements of theForecastLineDetailFactDataType class in one embodiment. These dataelements include bestCaseAmount 1002, costAmount 1004,productPriceAmount 1006, revenueAmount 1008, and worstCaseAmount 1010.

The revenueAmount data element 1008 specifies the total revenue that isexpected to be generated by the product. The bestCaseAmount data element1002 specifies the best case estimate of the revenue. The costAmountdata element 1004 specifies the total cost of the product. TheproductPriceAmount data element 1006 specifies the unit price of theproduct. The worstCaseAmount data element 1010 specifies the worst caseestimate of the revenue.

FIG. 11 illustrates the data elements of the RelatedAccount class in oneembodiment. The data elements used in the RelatedAccount class includeid 1102, communicationData 1104, listOfAddress 1108, listOfRelationship1110, customPartyData 1116, baseData 1118, and customData 1120.

FIG. 12 illustrates the data elements of the RelatedProduct class in oneembodiment. These data elements include id. 1202, baseData 1204,salesData 1206, configurationData 1208, relatedProductLine 1210,listOfPriceType 1212, listOfRelatedInvLoc 1214, listOfRelatedProduct1216, listOfRelatedBusUnit 1218, and customData 1220.

The id data element 1202 specific a unique identifier of the product.The baseData data element 1204 contains general information about theproduct. The salesData data element 1206 contains information pertainingto product sales. The configurationData data element 1208 containsinformation on product configuration. The relatedProductLine dataelement 1210 specifies the related product line. The listOfPriceTypedata element 1212 defines the collection of valid price types for thisproduct. The listOfRelatedInvLoc data element 1214 defines a collectionof inventory locations that stock this product. The locations mayinclude warehouses or plants. The listOfRelatedProduct data element 1216defines the list of related products. The listOfRelatedBusUnit dataelement 1218 contains information on organizations that are authorizedto sell this product.

FIG. 13 illustrates the data elements of the RelatedSalesRep class forthe person type, according to one embodiment. The data elements used inthe RelatedSalesRepresentative class include id 1302, communicationData1304, listOfAddress 1308, listOfRelationship 1310customPartyData 1316,baseData 1318, and customData 1322.

FIG. 14 illustrates the data elements of the relatedDemandSource classin one embodiment. These data elements include relatedOpportunity 1502and relatedServiceRequest 1504. The relatedOpportunity data element 1502contains information on a business opportunity for which the forecast iscreated, as will be discussed in more detail below in conjunction withFIG. 15. The relatedServiceRequest data element 1504 containsinformation on a service request for which the forecast is created, aswill be discussed in more detail below in conjunction with FIG. 16.

FIG. 15 illustrates the data elements of the relatedServiceRequest classin one embodiment. These data elements include id 1502, baseData 1504,relatedParentArea 1506, relatedRootArea 1508, relatedContract 1510,listOfRelatedContract 1512, listOfRelatedAccount 1514, listOfRelatedOwner 1516, statusData 1518, relatedProduct 1520,relatedInstalledProduct 1522, relatedBusUnit 1524, listOfRelatedActivity1526, and customData 1528.

The id data element 1502 may contain a unique identifier of the servicerequest. The baseData data element 1504 contains general information onthe service request. The relatedParentArea data element 1506 containsinformation about the area that triggered the creation of the servicerequest. The relatedRootArea data element 1508 identifies the area ofthe service. The relatedContract data element 1510 identifies thecontract for the performance of the service. The listOfRelatedContractdata element 1512 identifies a list of related contracts. ThelistOfRelatedAccount data element 1514 contains information on the listof related accounts. The listOfRelatedOwner data element 1516 containsinformation on the list of owners of the service request. The statusDatadata element 1518 defines the status of the service request. TherelatedProduct data element 1520 contains information on the relatedproduct. The relatedInstalledProduct data element 1522 containsinformation on the related installed product. The relatedBusUnit dataelement 1524 identifies a related business unit. ThelistOfRelatedActivity data element 1526 contains information on the listof related activities. The customData data element 1528 initiallycontains no data elements, but custom data elements can be added bydefining data elements in the ServiceRequestCustomData Type.

FIG. 16 illustrates the data elements of the relatedOpportunity class inone embodiment. These data elements include id 1602, baseData 1604,revenueData 1606, statusData 1608, listOfNotes 1610, listOfActivity1612, listOfAddress 1614, listOfRevenueItem 1616, relatedParentOpportunity 1618, listOfRelatedContact 1620, listOfRelatedAcount 1622,listOfRelatedSalesConsultant 1624, listOfRelatedSourcePartner 1626,listOfRelatedDestinationPartner 1628, and customData 1630.

The id data element 1602 may contain a unique identifier of theopportunity. The baseData data element 1604 contains general informationon the opportunity. The revenueData data element 1606 defines therevenue associated with the opportunity. The statusData data element1608 contains information on the status of the opportunity. ThelistOfNotes data element 1610 specifies notes associated with theopportunity. The listOfActivity data element 1612 specifies activitiesassociated with the opportunity. The listOfAddress data element 1614specifies the list of addresses associated with the opportunity. ThelistOfRevenueItem data element 1616 identifies the list of revenue itemsassociated with the opportunity. The relatedParent Opportunity dataelement 1618 contains information on the parent opportunity. ThelistOfRelatedContact data element 1620 contains information on thecontacts associated with the opportunity. The listOfRelatedAcount dataelement 1622 identifies accounts (producers and customers) associatedwith the opportunity. The listOfRelatedSalesConsultant data element 1624contains information on the sales employees and consultants related tothe opportunity. The listOfRelatedSourcePartner data element 1626contains information on the opportunity source partner or brand owner.The listOfRelatedDestinationPartner data element 1628 containsinformation on the opportunity target partner. The customData dataelement 1630 initially contains no data elements, but custom dataelements can be added by defining data elements in theOpportunityCustomData Type.

FIG. 17 is a block diagram of an exemplary computer system 1700 (e.g.,of the integration server 200 of FIG. 2) that may be used to perform oneor more of the operations described herein. In alternative embodiments,the machine may comprise a network router, a network switch, a networkbridge, Personal Digital Assistant (PDA), a cellular telephone, a webappliance or any machine capable of executing a sequence of instructionsthat specify actions to be taken by that machine.

The computer system 1700 includes a processor 1702, a main memory 1704and a static memory 1706, which communicate with each other via a bus1708. The computer system 1700 may further include a video display unit1710 (e.g., a liquid crystal display (LCD) or a cathode ray tube (CRT)).The computer system 1700 also includes an alpha-numeric input device1712 (e.g., a keyboard), a cursor control device 1714 (e.g., a mouse), adisk drive unit 1716, a signal generation device 1720 (e.g., a speaker)and a network interface device 1722.

The disk drive unit 1716 includes a computer-readable medium 1724 onwhich is stored a set of instructions (i.e., software) 1726 embodyingany one, or all, of the methodologies described above. The software 1726is also shown to reside, completely or at least partially, within themain memory 1704 and/or within the processor 1702. The software 1726 mayfurther be transmitted or received via the network interface device1722. For the purposes of this specification, the term“computer-readable medium” shall be taken to include any medium that iscapable of storing or encoding a sequence of instructions for executionby the computer and that cause the computer to perform any one of themethodologies of the present invention. The term “computer-readablemedium” shall accordingly be taken to included, but not be limited to,solid-state memories, optical and magnetic disks, and carrier wavesignals.

Whereas many alterations and modifications of the present invention willno doubt become apparent to a person of ordinary skill in the art afterhaving read the foregoing description, it is to be understood that anyparticular embodiment shown and described by way of illustration is inno way intended to be considered limiting. Therefore, references todetails of various embodiments are not intended to limit the scope ofthe claims which in themselves recite only those features regarded asessential to the invention.

1. A computer-implemented method comprising: defining a forecast classthat represents forecasts of different types, the forecast classidentifying a set of relationships of a forecast with a plurality ofentities related to the forecast; and representing the forecastsprovided by the forecast class in a common format.
 2. The method ofclaim 1 wherein the different forecast types comprise a demand forecasttype and a supply forecast type.
 3. The method of claim 2 wherein theforecast class includes a designated data element to store dataindicating whether data elements of the forecast class store data of ademand forecast.
 4. The method of claim 1 wherein the plurality ofentities related to the forecast includes entities selected from thegroup consisting of a related business unit, a forecast planner, arelated account, a related product, a related sales representative, arelated demand source, and related fact data.
 5. The method of claim 4wherein the related demand source is any one of a related demand sourceand a related opportunity.
 6. The method of claim 4 wherein the relatedfact data includes data items selected from the group consisting of aforecast best case amount, a forecast worst case amount, a forecast costamount, a forecast product price amount, and a forecast revenue amount.7. The method of claim 1 wherein the forecast class includes a customdata element for defining one or more custom data fields for theforecast class.
 8. The method of claim 7 wherein the one or more customdata fields of the forecast class are specific to an application.
 9. Themethod of claim 1 further comprising: instantiating the forecast class;and initializing data elements of the instantiated forecast class. 10.The method of claim 9 further comprising: transforming data receivedfrom a source application into a common format of the forecast class;transforming the data from the common format into a target format of atarget application; and sending the data in the target format to thetarget application.
 11. The method of claim 1 wherein a definition ofthe forecast class is represented as an XML schema.
 12. Acomputer-implemented method for data transformation, the methodcomprising: receiving forecast data from a source application; andtransforming the forecast data into a common format provided by aforecast class, wherein the forecast class represents forecasts ofdifferent types and identifies a set of relationships of a forecast witha plurality of entities related to the forecast.
 13. The method of claim12 wherein the different forecast types comprise a demand forecast typeand a supply forecast type.
 14. The method of claim 13 furthercomprising: determining that the forecast data received from the sourceapplication pertains to a forecast of a supply type; and setting adesignated data element of the forecast class to indicate that dataelements of the forecast class store data of a supply forecast.
 15. Themethod of claim 12 wherein the plurality of entities related to theforecast includes entities selected from the group consisting of arelated business unit, a forecast planner, a related account, a relatedproduct, a related sales representative, a related demand source, andrelated fact data.
 16. The method of claim 15 wherein the related demandsource is any one of a related demand source and a related opportunity.17. The method of claim 16 wherein the related fact data includes dataitems selected from the group consisting of a forecast best case amount,a forecast worst case amount, a forecast cost amount, a forecast productprice amount, and a forecast revenue amount.
 18. The method of claim 12wherein the forecast class includes a custom data element for definingone or more custom data fields for the forecast class.
 19. Acomputer-readable medium having executable instructions to cause acomputer to perform a method comprising: defining a forecast class thatrepresents forecasts of different types, the forecast class identifyinga set of relationships of a forecast with a plurality of entitiesrelated to the forecast; and representing the forecasts provided by theforecast class in a common format.
 20. The computer readable medium ofclaim 19 wherein the different forecast types comprise a demand forecasttype and a supply forecast type.
 21. The computer readable medium ofclaim 20 wherein the forecast class includes a designated data elementto store data indicating whether data elements of the forecast classstore data of a demand forecast.
 22. The computer readable medium ofclaim 19 wherein the plurality of entities related to the forecastincludes entities selected from the group consisting of a relatedbusiness unit, a forecast planner, a related account, a related product,a related sales representative, a related demand source, and relatedfact data.
 23. The computer readable medium of claim 19 wherein theforecast class includes a custom data element for defining one or morecustom data fields for the forecast class.
 24. The computer readablemedium of claim 19 wherein a definition of the forecast class isrepresented as an XML schema.
 25. A computer readable medium havingexecutable instructions to cause a computer to perform a methodcomprising: receiving forecast data from a source application; andtransforming the forecast data into a common format provided by aforecast class, wherein the forecast class represents forecasts ofdifferent types and identifies a set of relationships of a forecast witha plurality of entities related to the forecast.
 26. The computerreadable medium of claim 25 wherein the different forecast typescomprise a demand forecast type and a supply forecast type.
 27. Thecomputer readable medium of claim 25 wherein the plurality of entitiesrelated to the forecast includes entities selected from the groupconsisting of a related business unit, a forecast planner, a relatedaccount, a related product, a related sales representative, a relateddemand source, and related fact data.
 28. The computer readable mediumof claim 25 wherein the forecast class includes a custom data elementfor defining one or more custom data fields for the forecast class. 29.A system comprising: a memory; and at least one processor coupled to thememory, the processor executing a set of instructions which cause theprocessor to: define a forecast class that represents forecasts ofdifferent types, the forecast class identifying a set of relationshipsof a forecast with a plurality of entities related to the forecasts, andrepresent the forecasts provided by the forecast class in a commonformat.
 30. The system of claim 29 wherein the different forecast typescomprise a demand forecast type and a supply forecast type.
 31. Thesystem of claim 29 wherein the plurality of entities related to theforecast includes entities selected from the group consisting of arelated business unit, a forecast planner, a related account, a relatedproduct, a related sales representative, a related demand source, andrelated fact data.
 32. A system comprising: a memory; and at least oneprocessor coupled to the memory, the processor executing a set ofinstructions which cause the processor to: receive forecast data from asource application, and transform the forecast data into a common formatprovided by a forecast class, wherein the forecast class representsforecasts of different types and identifies a set of relationships of aforecast with a plurality of entities related to the forecast.
 33. Thesystem of claim 32 wherein the different forecast types comprise ademand forecast type and a supply forecast type.
 34. The system of claim32 wherein the plurality of entities related to the forecast includesentities selected from the group consisting of a related business unit,a forecast planner, a related account, a related product, a relatedsales representative, a related demand source, and related fact data.35. An apparatus comprising: means for defining a forecast class thatrepresents forecasts of different types, the forecast class identifyinga set of relationships of a forecast with a plurality of entitiesrelated to the forecast; and means for representing the forecastsprovided by the forecast class in a common format.
 36. An apparatus fordata transformation, the apparatus comprising: means for receivingforecast data from a source application; and means for transforming theforecast data into a common format provided by a forecast class, whereinthe forecast class represents forecasts of different types andidentifies a set of relationships of a forecast with a plurality ofentities related to the forecast.
 37. The method of claim 1 furthercomprising: transforming the forecast into a first transformed forecastin the common format provided by the forecast class; and transformingthe first transformed forecast into a second transformed forecast in aformat recognizable by a target system.
 38. The method of claim 12,wherein a first transformed data comprises the forecast data transformedinto the common format, the method further comprising: transforming thefirst transformed data into a second transformed data in a formatrecognizable by a target system.
 39. The computer readable medium ofclaim 19, wherein the method further comprises: transforming theforecast into a first transformed forecast in the common format providedby the forecast class; and transforming the first transformed forecastinto a second transformed forecast in a format recognizable by a targetsystem.
 40. The computer readable medium of claim 25, wherein a firsttransformed data comprises the forecast data transformed into the commonformat, and the method further comprises: transforming the firsttransformed data into a second transformed data in a format recognizableby a target system.
 41. The system of claim 29, wherein the instructionsfurther cause the processor to: transform the forecast into a firsttransformed forecast in the common format provided by the forecastclass; and transform the first transformed forecast into a secondtransformed forecast in a format recognizable by a target system. 42.The system of claim 32, wherein a first transformed data comprises theforecast data transformed into the common format, and the instructionsfurther cause the processor to: transform the first transformed datainto a second transformed data in a format recognizable by a targetsystem.
 43. The apparatus of claim 35 further comprising: means fortransforming the forecast into a first transformed forecast in thecommon format provided by the forecast class; and means for transformingthe first transformed forecast into a second transformed forecast in aformat recognizable by a target system.
 44. The apparatus of claim 36,wherein a first transformed data comprises the forecast data transformedinto the common format, the apparatus further comprising: means fortransforming the first transformed data into a second transformed datain a format recognizable by a target system.